Monitoring and determining the state of health of a user

ABSTRACT

A method for identifying a change associated with a state of health of a user. In one embodiment, the method includes at least one computer processors receiving monitoring data associated with monitoring a user, where the monitoring data is generated by one or more sensors. The method further includes determining a state of health of the monitored user by analyzing the monitoring data utilizing one or more models. The method further includes determining a level of urgency based, at least in part, upon the determine state of health of the monitored user. The method further includes transmitting one or more respective notifications to one or more devices based, at least in part, on the determined state of health of the user and the corresponding level of level of urgency, and where a notification includes a determined state of health and the corresponding determined state of urgency associated with the monitored user.

BACKGROUND OF THE INVENTION

The present invention relates generally to the field of medical dataacquisition and analysis, and more particularly to monitoring andanalyzing physiological information related to a user to identifypotential medical issues.

There are a variety of methods, known in the art, for monitoring themechanics of eating and swallowing (i.e., deglutition) of an individualwith a medical condition. A set of sounds produced during eating andswallowing can be processed and analyzed to aid in diagnosing a state ofhealth for some aspect of an individual. Similarly, there are mechanismsand models that can describe or analyze the bio-mechanics of theconsumption of food and/or beverages based on various quantitativemeasures, such as temperature, consistency, quantity, etc. In addition,models associated with the consumption of food and/or beverages arefurther affected by non-quantitative parameters, such as taste,presentation (e.g., an appetizing appearance, and user likes/dislikes.

Individuals with known medical conditions may be advised by medicalprofessionals to identify various actions associated with eating and/ordrinking that can signal a negative health effect for the individual.Alternatively, medical professionals can request the individual to keepa personal journal and upon review of the personal journal the medicalprofessionals may advise the individual of apparent changes that signala worsening of the medical condition. In some instances, monitoring theeating and swallowing behaviors of an individual within a controlledsetting, such as a hospital, an office of a doctor, a skilled nursingfacility, or within a residence by a trained home healthcare workerprovides one set of diagnostic information. In other instances, suchcontrolled settings are less conducive to frequent monitoring of anindividual, affects the quality of life of the individual, and may lessaccurately reflect the eating and swallowing behaviors of theindividual.

SUMMARY

According to aspects of the present invention, there is a method,computer program product, and/or system for identifying a changeassociated with a state of health of a user. In an embodiment, themethod includes receiving monitoring data associated with monitoring auser, where the monitoring data is generated by one or more sensors. Themethod further includes determining a state of health of the monitoreduser by analyzing the monitoring data utilizing one or more models. Themethod further includes determining a level of urgency based, at leastin part, upon the determined state of health of the monitored user. Themethod further includes transmitting one or more respectivenotifications to one or more devices based, at least in part, on thedetermined state of health of the user and the corresponding level ofurgency, where the one or more devices includes a device associated withthe monitored user, and where a notification includes a determined stateof health and the corresponding determined state of urgency associatedwith the monitored user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a networked computing environment, in accordance withan embodiment of the present invention.

FIG. 2 depicts a flowchart of the operational steps of a user baselineprogram, in accordance with an embodiment of the present invention.

FIG. 3 depicts a flowchart of the operational steps of a medicalmonitoring program, in accordance with an embodiment of the presentinvention.

FIG. 4 is a block diagram of components of a computer, in accordancewith an embodiment of the present invention.

DETAILED DESCRIPTION

Embodiments of the present invention recognize that a set of soundsproduced during eating and swallowing can be processed and analyzed toaid in diagnosing a state of health for some aspect of an individual.Some medical devices for monitoring the mechanics of eating andswallowing can be bulky, obvious, uncomfortable, and/or somewhatinvasive for a user to utilize. As such, a user with a known medicalcondition or a potential medical condition may avoid utilizingmonitoring devices until monitoring becomes an imperative, thusincreasing a likelihood of negative effects to a user. Similarly,embodiments of the present invention recognize that there are a varietyof methods, known in the art, for identifying and monitoring theconsumption of food by an individual. For example, some mobile deviceapplications (apps) can utilize various image recognition techniques todetermine the type of food and/or beverage that an individual consumesand can estimate a quantity of food and/or beverage consumed.

Embodiments of the present invention also recognize that users areindividuals and that the monitored effects of ingesting and consumingthe same type, quantity, and variety of food or beverage will differamong individuals as well as differing with time for an individual basedon various personal, health, and environmental factors, such as a usersuffering from allergies may produce different monitoring data for thesame food based on a severity of an episode of an allergy. In oneexample, the allergies of a user may produce sinus drainage that affectsswallowing. In another example, the allergies of a user may producebreathing restrictions that reduce the taste of food and change thechewing and swallowing behaviors of the user, thereby modifying thesounds produced by chewing and swallowing. Individuals can be reluctantto seek medical diagnosis for seemingly minor changes that affectresponses to daily activities, such as eating and/or a change to anappearance of an individual. As such, medical issues that developgradually may be ignored in response to an individual discounting achange as “getting older” or misconstruing cause and effect associatedwith a physiological or health based change. In one example, anindividual may modify a behavior to accommodate a change, such aschewing smaller portions (e.g., size) of food or cutting food intosmaller pieces to ease the actions of swallowing, or sipping a beverageas opposed to previously gulping the beverage. In another example, auser may attribute a perceived change to eating or swallowing on thetype or nature of a food or beverage, such as the coffee is too hot, thelack of ripeness of a fruit, or the preparation of the food. Delayingthe identification of a medical issue can negatively affect economic andmedical outcomes of an individual.

Therefore, embodiments of the present invention generate a plurality ofmodels to describe various physiological responses and mechanismsrelated to consuming food and beverages under various conditions (e.g.,normal behaviors, during an unrelated illness, etc.) by variousindividuals. Various models utilize sounds and other informationreceived from a plurality of sensors as input. Some models areassociated with various types of information that are obtained relatingto the jaw, neck, and throat areas of a user that is monitored, such asskin (i.e., epidermis) color, texture, swelling, lumps, etc. Othermodels represent characteristics and responses related to the texture,consistency, temperature, etc. of food and beverages consumed as opposedto models associated with a user. Some embodiments of the presentinvention include interaction among models and/or combine multiplemodels (e.g., analytical workloads) to produce a more complex model,such as a graph workload that are utilized to determine a current stateof health of the user and/or predict a change to a future state ofhealth of the user.

Embodiments of the present invention can utilize various models of aplurality of users and a plurality of models related to consumed foodand beverages, sans user information (e.g., personal information,regulated information) that are associated with various normal andabnormal medical conditions to compare and contrast the monitoring(e.g., sensor) information of a user to determine whether the monitoringinformation of the user indicates an unfavorable change to the state ofhealth of the user. Embodiments of the present invention utilize themodels and monitoring information to assess a degree of urgencyassociated with determining an unfavorable change related to the stateof health of the user.

Embodiments of the present invention utilize multiple sensors andfeedback mechanisms to obtain monitoring data and related contextualinformation. Embodiments of the present invention utilize analytics,machine learning, and cognitive methods to analyze monitoring data,predict changes to a state of health of the user, and obtain feedbackfrom the user. Feedback from the user is utilized to generate, refine,and update one or more models that represent or predict various statesof health associated with the user and/or models that represent theconsumption of various foods and beverages. Embodiments of the presentinvention utilize a plurality of medical databases, reports, medicalstudies, and diagnostic information to generate models that can identifyand flag potential medical issues.

Some sensors utilized by embodiments of the present invention directlymonitor internal and external aspects of a user while the user consumesfood and/or beverage, such as temperature, pressure, sounds, and/ormovement of muscle and bone. Other sensors utilized by embodiments ofthe present invention indirectly monitor internal and external aspectsof a user while the user consumes food and/or beverage. For example, acamera of a smartphone can capture images of the food and clothing ofthe user, and other sensors can monitor the environment in proximity tothe user, such as air temperature. Clocks and calendars can also supplypertinent information that is utilized by embodiments of the presentinvention, such as adjusting an expected result of a model based on timeand date. For example, during a workweek, a user may rush through eatingbreakfast and lunch in contrast with a weekend or a period of vacationduring which the user eats at a more leisurely pace generating differentresults associated with similar consumption of food and beverages.

Embodiments of the present invention can utilize a plurality of sensors(e.g., monitoring components) that can be included on, about, and/orwithin a user. Various embodiments of the present invention utilize adistribution of sensors to improve the comfort, quality of life, andappearance (e.g., discreetly worn, camouflaged, etc.) of a user. Assuch, a user is more likely to utilize embodiments of the presentinvention due to a reduced obtrusiveness of sensors monitoring the user.Embodiments of the present invention can utilize sensors included injewelry, such as a necklace; in apparel, such as a necktie; attachableto an item of apparel, such as within the collar of a shirt; or withinthe earpieces of a pair of glasses. Other embodiments of the presentinvention utilize one or more sensors embedded within a dental device,such as dentures or replacement teeth attached to dental implants. Inaddition, sensors embedded within a dental device can acquire moredirect monitoring information about the temperature and consistency ofitems consumed by a user or a non-food item present in the mouth orthroat of a user, such as a foreign object.

Embodiments of the present invention are not limited to the consumptionof food or beverages. Some embodiments of the present invention can beutilized to monitor the jaw, neck, throat, and deglutition of a userunder other conditions, which may indicate that the user is in distressand requires medical assistance. In an example, embodiments of thepresent invention may be utilized to identify an anaphylaxis reaction, aseizure, a fit of choking, an injury, an exposure to something toxic, achange to a severity of a disease within the throat, and/or a responseto an equipment failure (e.g., personal protective equipment, a supplyof fresh air, etc.).

Further, embodiments of the present invention recognize that bycombining information from personal computing devices and medicalInternet-of-things (IoT) sensors; cognitive, analytics, and machinelearning; and a corpus of knowledge, models can be created to predict astate of health of a user and respond by notifying one or moreindividual by various devices that a change to the health of the user ispredicted. Utilizing information and feedback provided by the user,embodiments of the current invention improve the scope and accuracy ofmodels associated with the user. In addition, by anonymizing andaggregating information, feedback, and models of a plurality of users ata system (e.g., as a service) level, embodiments of the presentinvention continually refine and improve the accuracy of predictions ofstates of health of a user. As such, monitoring a user, predicting astate of health, and notifying appropriate individuals while reducingthe intrusiveness of sensors and improving the quality-of-life of a useris seen to be improved in at least these aspects. Also, by implementingaspects of the invention across a networked computing environment, morecomplex models associating with predicting the health of the user can beutilized in near real time as opposed to the models that can executewithin the personal device of the user or utilize data uploaded during avisit to a doctor.

The present invention will now be described in detail with reference tothe Figures. FIG. 1 is a functional block diagram illustrating networkedcomputing environment 100, in accordance with embodiments of the presentinvention. In an embodiment, networked computing environment 100includes: system 102, device 120, device 130, device 140, and sensors150 all interconnected over network 110. In some embodiments, networkedcomputing environment 100 includes one or more instances of device 120,device 130, device 140, and/or sensors 150. In one embodiment, networkedcomputing environment 100 includes communication path 112 (dashed line),such as near-field communications or wireless communications that linkone or more instances of sensors 150 to device 130. In anotherembodiment, networked computing environment 100 includes communicationpath 114 (long dash, double dot line), such as wireless communicationsthat links one or more instance of sensors 150 to another system (e.g.,system 102) or a device within networked computing environment 100 vianetwork 110. Many modifications to the depicted environment may be madeby those skilled in the art without departing from the scope of theinvention as recited by the claims.

System 102, device 120, device 130, and device 140 may be: laptopcomputers, tablet computers, netbook computers, personal computers (PC),desktop computers, personal digital assistants (PDA), smartphones,wearable devices (e.g., digital eyeglasses, smart glasses, smartwatches, smart televisions, etc.), or any programmable computer systemsknown in the art. In certain embodiments, system 102, device 120, device130, and device 140 represent computer systems utilizing clusteredcomputers and components (e.g., database server computers, applicationserver computers, etc.) that act as a single pool of seamless resourceswhen accessed through network 110, as is common in data centers and withcloud-computing applications. In some embodiments, device 130 isrepresentative of two or more linked computing devices that sharecomputing resources, such as digital eyeglasses and a smartphone, or apersonal fitness/medical device and a tablet computer. In general,system 102, device 120, device 130, and device 140 are representative ofany programmable electronic device or combination of programmableelectronic devices capable of executing machine-readable programinstructions and communicating with users of system 102, device 120,device 130, and device 140, via network 110. System 102, device 120,device 130, and device 140 may include components, as depicted anddescribed in further detail with respect to FIG. 4, in accordance withembodiments of the present invention. In various embodiments, sensors150 includes various electronic, computing, and networking capabilities,as depicted and described in further detail with respect to FIG. 4, inaccordance with embodiments of the present invention.

System 102 includes: storage 103, analytics suite 106, machine learningprogram 107, user baseline program 200, and medical monitoring program300. System 102 further includes user data 104 and models 105 withinstorage 103. In some embodiments, system 102 accesses/subscribes to oneor more computing programs and/or databases utilized by one or moreembodiments of the present invention, such as cognitive or expert systemwithin another computing system (not shown) that is network accessibleand/or one or more instances of medical database(s) 125. Storage 103includes user data 104 and models 105. In an embodiment, storage 103 mayalso include various programs and/or databases, such as, but not limitedto: an operating system, a file management program, a databasemanagement system, an e-mail program, visualization software, web-basedapplications, etc. (not shown) utilized by system 102. In variousembodiments, system 102 is a cognitive computing environment.

User data 104 includes a plurality of profiles of users that utilizesystem 102 to generate analytical and predictive models utilized fordetermining a state of health, or predicting a change associated withthe state of health of a user based on monitoring data corresponding tothe user. User data 104 can also include and catalog other data relatedto a monitored user, such as information related to one or more medicalprofessionals or services utilized by the user, emergency contactinformation (e.g., alternate phone numbers, e-mail addresses), insuranceinformation, etc. In an embodiment, user data 104 is structured as adatabase that includes structured and unstructured informationrespectively associated with the plurality of users that utilizeembodiments of the present invention. User data 104 may also include acatalog, cross-reference, associative array, table, etc. that links oneor more models within models 105 to one or more users identified withinuser data 104.

In one embodiment, individual user profiles and data within user data104 may include: demographic data; information associated with thephysical condition of the user, such as height, weight, current medicalissues; habits and activities of the user; dietary information; and/oridentified risk factors, such as genetic testing data, previous exposureto environmental toxins, diagnosed medical conditions, job-related riskfactors (e.g., known exposure items, potential exposure items). In someembodiments, user data 104 periodically receives data (e.g., one or moreportions of user data 134) uploaded by device 130. In other embodiments,user data 104 periodically receives information, such as medical testinginformation (e.g., blood work, medical imaging data, etc.), and resultsof one or more visits to medical professionals from another computingsystem or device (e.g., device 140).

In various embodiments, user data 104 includes a corpus of structuredand unstructured data that is stored within one or more databases. Insome scenarios, user data 104 stores and/or catalogs a plurality ofinstances of unprocessed information (e.g., raw data) obtained by one ormore instances of sensors 150 that are associated with a user of device130. In other scenarios, user data 104 stores a plurality of instancesof information obtained by one or more instances of sensors 150 that areassociated with a user of device 130 that is processed by sensor dataprocessing program 108. In one example, processed data from an instanceof sensors 150 may be described via a Fourier transform of filteredaudio signal related to a user swallowing. In another example, processedsensor (e.g., monitoring data) data within user data 104 may describeconsistency information, such as a duration of chewing, a value of thepressure exerted, types of jaw motions, etc. associated with foodconsumed by a user of device 130 prior to swallowing the food. Inaddition, user data 104 can include information related to the foodassociated with the consistency information, such as a type of food, aquantity of food, a preparation method for the food, etc.

Models 105 includes a plurality of models related to individual users,consumptions of food and beverages, various medical conditions, etc.Models 105 may include, but are not limited to: deterministic models,probabilistic models, statistical models, stochastic models, decisiontrees, etc., or a combination thereof. Some models within models 105 maybe dynamically generated and executed based on various rules utilized bymachine learning program 107, such as utilizing information from variousinstances of sensors to perform a graph database analysis. Other modelsmay dictate the aspects of sensor data processing program 108 or 138that are to utilize process information from various instances ofsensors 150.

In one embodiment, models 105 includes models of a plurality of usersthat were generated and refined over time, and based on informationobtained by various instances of user baseline program 200. Some modelswithin models 105 are initial models or training models derived from oneor more individuals that participated in medical studies undercontrolled conditions to generated models associated with various typesof food and beverages consumed based on texture, consistency,temperature, seasoning (e.g., salt-restrictive, bland, spicy, etc.),and/or preparation method (e.g., baked, fried, raw, etc.). In anotherembodiment, various models included in models 105 are utilized bymedical monitoring program 300 to identify changes to a monitored user.

In various embodiments, models 105 includes models based on aggregatedand anonymized data. In one scenario, models 105 includes consistencymodels for a plurality of foods. In one example, models 105 can includemodels related to food and drinks, such as potatoes prepared as crispchips, with the crisp chips having a consistency model different frommashed potatoes with gravy. In another scenario, models 105 includesmodels that can identify and describe various medical conditions andassociated levels of severity and progress (e.g., changes, evolution) ofthe medical conditions. Some models that determine or predict a state ofhealth of a user may be generalized and utilize results of other modelstuned to individual users.

Analytics suite 106 includes, but is not limited to: analytic functions,cognitive functions (e.g., image recognition, natural languageprocessing, facial recognition, expression analysis, etc.), inferentialreasoning programs, statistical analysis programs, a contextual analysisprogram, a database query generator, etc. In one embodiment, sensor dataprocessing program 108 utilizes one or more aspects of analytics suite106 to determine information associated with information received frominstances of sensors 150, such as determining a distribution ofoccurrences and identifying statistical outliers that can bias a model.In some embodiments, aspects of analytics suite 106 are utilized bymachine learning program 107 to generate a model. In other embodiments,aspects of analytics suite 106 are utilized by user baseline program 200and/or medical monitoring program 300 to parse and analyze informationinput by a user.

Machine learning program 107 is a suite of techniques and algorithmsutilized to generate and modify various models that are subsequentlystored within models 105 of system 102 and/or user data 134 withindevice 130. Machine learning program 107 generates and/or modifiesmodels that determine a state of health of a user, predicts a change toa future state of health of the user, and creates models that determineaspects of the food and/or beverages (e.g., consistency) consumed by theuser based on information input by a user and/or received from varioussensors. Some features of the models generated and/or modified bymachine learning program 107 are previously discussed with respect tomodels 105.

Examples of types of models generated or modified by machine learningprogram 107 include, but are not limited to: graph databases,algorithms, decision trees, expert system, and/or cognitive systems. Insome embodiments, machine learning program 107 includes: support vectormachines, artificial neural networks, naïve Bayes classifiers,predictive analytics, and other machine learning techniques/algorithmsknown in the art. In another embodiment, various aspects of analyticssuite 106 are utilized to augment various functions of machine learningprogram 107. In an example, system 102 may utilize analytics suite 106to determine relationships and interactions among data received frominstances of sensors 150 and/or information input by the user.Subsequently, machine learning program 107 utilizes information frommedical database(s) 125 and the determined relationships andinteractions to generate models that can determine a current state ofhealth of a user and/or predict a future change to the state of healthof the user.

Sensor data processing program 108 includes a suite of functions andprograms to analyze information received from various instances ofsensors 150 to extract pertinent data (e.g., preprocess) for input toone or more models of a user. In one example, sensor data processingprogram 108 may include: audio filters, signal processing algorithms,conversion routines (e.g., resistance to pressure, voltage to speed,etc.). In another example, sensor data processing program 108 cananalyze infrasound and ultrasonic information to determine positions offood and/or various aspects of the physiology of the user. Some aspectsof sensor data processing program 108 process visual information andinterface with analytics suite 106 to further identify and contextualizeother information, such as image recognition to identify skin color,texture, patterning, skin irregularities (e.g., a lump); identify foodprior to consumption; apparel of a user; etc.

User baseline program 200 is a program that generates and/or updatesmodels utilized to determine a state of health or to predict a change tothe state of health of a monitored user. User baseline program 200utilizes information obtained from one or more instances of sensors 150,on, about, or within the user. User baseline program 200 utilizesvarious data processing, cognitive, and/or analytics programs to processthe information received from one or more instances of sensors 150 andinformation input by the user to generate models related to the user. Invarious embodiments, user baseline program 200 utilizes one or moremodels of users similar to the user as initial models that evolve andbetter represent the user as a corpus of information for the monitoredincreases with time. In some embodiments, an instance of user baselineprogram 200 is utilized by an instance of medical monitoring program 300to modify one or more models related to the user utilizing variousmachine learning techniques to process the information received from oneor more sensors and information input from the user (e.g., feedback).

In one embodiment, user baseline program 200 generates or updates modelsbased on deglutition by a user and information, input by the user,associated with the deglutition. In another embodiment, user baselineprogram 200 utilizes information from other instances of sensors 150visual information associated with the user, such as skin color. In someembodiments, user baseline program 200 queries a user to obtaincontextual information utilized to analyze and interpret informationfrom one or more instances of sensors 150 and/or contextual informationto validate or modify the results of a model. In an example, userbaseline program 200 may utilize various aspects of system 102 to accesspredefined questionnaires to be presented to a user, or dynamicallygenerate various elements within a questionnaire based on a model used,sensor information, information related to the user, and/or informationassociated with one or more medical conditions of the user.

Instances of user baseline program 200 execute on device 130, system102, or a combination thereof based on network accessibility and/orcomputational requirements of one or more models. In an example, if userbaseline program 200 utilizes analytics suite 106 and/or machinelearning program 107, then an instance of user baseline program 200 mayexecute within system 102 to reduce delays associated with network 110.In one scenario, user baseline program 200 executes locally on device130 of the user. In another scenario, an instance of user baselineprogram 200 executes remotely on system 102 and receives information andfeedback from one or more sources, such as the user of device 130 or amedical professional utilizing device 140.

In other embodiments, user baseline program 200 executes in response toone or more dictated commands. In an example, a medical professionalutilizes UI 142 to execute an instance of user baseline program 200 toobtain additional information from a user based on a response generatedby a model during the execution of an instance of medical monitoringprogram 300. In a further embodiment, an instance of user baselineprogram 200 executes in response to system 102 determining that newmedical information (e.g., clinical studies, diagnoses, etc.) isavailable within medical database(s) 125 of device 120 that affects oneor more models of various users. System 102 may also dictate theexecution of an instance of user baseline program 200 to compare andcontrast models of different users, as new machine learning/modelingalgorithms are developed, etc.

Medical monitoring program 300 is a program for determining orpredicting a change to a state of health of a user based on informationassociated with the neck and throat regions of the user. Medicalmonitoring program 300 inputs information associated with variousinstances of sensors 150 to one or more models associated withmonitoring a state of health of the user. Some models utilized bymedical monitoring program 300 are generated or modified by an instanceof user baseline program 200. In some scenarios, an instance of medicalmonitoring program 300 executes on device 130 and utilizes one or moremodels included in user data 134 of device 130. In other scenarios, aninstance of medical monitoring program 300 executes on system 102 andutilizes one or more models included in models 105 of system 102.

In various scenarios, based on the computing capabilities of device 130,an instance of medical monitoring program 300 executing on device 130interacts with an instance of medical monitoring program 300 executingon system 102 to execute more complicated models and/or determine alevel of urgency associated with a state of health of the monitoreduser. A level of urgency associated with the state of health of the usercan range from: not urgent, a minor change to the health of the user isidentified and the user is notified; very low-level of urgency, a changeto the health of the user is identified with further monitoringindicated; low-level of urgency, consult a medical professional;moderate urgency, diagnostic testing and examination of the user isindicated and a doctor of the user is also notified; severe urgency,seek medical attention (i.e., visit an emergency room of a hospital); toa critical level of urgency where emergency response personnel aredispatched to the location of the user.

In one embodiment, medical monitoring program 300 utilizes modelsgenerated by user baseline program 200 to determine a change to a stateof health of a user based on information relating to the consistency offood and beverage consumed by a user and the sounds generated during theconsumption of food and beverage by the user. In another embodiment, ifmedical monitoring program 300 determines that a model does not describethe state of heath of the user, then medical monitoring program 300 candictate the execution of an instance of user baseline program 200 tomodify a model and/or obtain additional information from the user. Insome scenarios, medical monitoring program 300 pauses while userbaseline program 200 modifies a model. In some embodiments, medicalmonitoring program 300 can query medical database(s) 125 of device 120to obtain information utilized by one or more models and/or included inresults generated by one or more models. In various embodiments, basedon the results obtained from one or more models, medical monitoringprogram 300 determines a level of urgency associated with the state ofhealth of the user and communicates responses (e.g., notifications) tothe user, a medical professional associated with the user, and/or amedical service.

In one embodiment, system 102 communicates through network 110 to device120 and device 130. In some embodiments, system 102 communicates withone or more other computing systems and/or computing resources, such asa web server, an e-mail server, a network of health care serviceproviders, etc. (not shown) via network 110. Network 110 can be, forexample, a local area network (LAN), a telecommunications network, awireless local area network (WLAN), such as an intranet, a wide areanetwork (WAN), such as the Internet, or any combination of the previousand can include wired, wireless, or fiber optic connections. In general,network 110 can be any combination of connections and protocols thatwill support communications between system 102, device 120, and device130, in accordance with embodiments of the present invention. In variousembodiments, network 110 operates locally via wired, wireless, oroptical connections and can be any combination of connections andprotocols (e.g., personal area network (PAN), near field communication(NFC), laser, infrared, ultrasonic, etc.). In other embodiments, network110 includes communication path 114 that enables an instance of sensors150 to transmit data to system 102. Similarly, one or more aspects ofnetwork 110 may be utilized to generate communication path 112 to enableone or more instance of sensors 150 to transmit information to device130.

Device 130 may include user interface (UI) 132, storage 133, sensor dataprocessing program 138, user baseline program 200, and medicalmonitoring program 300. In some scenarios, device 130 is a computingdevice tailored for various medical monitoring functions associated witha user. In other scenarios, device 130 is a more common computing device(e.g., a smartphone, a tablet computer, etc.) utilized by a user that isadapted to include various medical monitoring functions for the user.Storage 133 may be comprised of a combination of volatile andnon-volatile storage media. Storage 133 includes user data 134 andsensor data 135. In addition, storage 133 also stores various programsand data (not shown) utilized by device 130. Examples of programs thatstorage 133 may include are: an operating system, a web browser, anoffice productivity suite, a communication program, a natural languageprocessing (NLP) program, one or more applications (apps), such as aninstant messaging (IM) app, a telephone app, and a video chat app, etc.Examples of data that storage 133 may include, but are not limited toare: user preferences, a web browsing history, video files, informationutilized to identify and locate device 130, etc. In some embodiments,device 130 utilizes network 110 to communicate with another computingsystem (not shown) to obtain environmental factors in proximity to theuser, such as temperature, barometric pressure, pollen count, etc.

In one embodiment, UI 132 may be a graphical user interface (GUI) or aweb user interface (WUI), and UI 132 can display text, documents, forms,web browser windows, user options, application interfaces, andinstructions for operation, and include the information, such asgraphic, text, and sound that a program presents to a user. In addition,UI 132 controls sequences/actions that the user employs to input and/ormodify user data, input data, and provide feedback to user baselineprogram 200, and/or respond to one or more notifications generated bymedical monitoring program 300. In various embodiments, UI 132 displaysone or more icons representing applications that a user can execute vianetwork 110, and various programs of system 102 and/or other computingsystems (not shown) accessible via network 110.

In some embodiments, a user of device 130 can interact with UI 132 via asingular device, such as a touch screen (e.g., display) that performsboth input to a GUI/WUI, and as an output device (e.g., a display)presenting a plurality of icons associated with apps and/or imagesdepicting one or more executing software applications. In otherembodiments, a software program (e.g., a web browser) can generate UI132 operating within the GUI environment of device 130. UI 132 acceptsinput from a plurality of input/output (I/O) devices (not shown)including, but not limited to, a tactile sensor interface (e.g., a touchscreen, a touchpad), a natural user interface (e.g., voice control unitor a motion capture device), and virtual or augmented reality interfacesutilizing on eye tracking, a cyberglove, a head-up display, etc. Inaddition to the audio and visual interactions, UI 132 may receive inputin response to a user of device 130 utilizing natural language, such aswritten words or spoken words, device 130 identifies as informationand/or commands.

User data 134 includes one or more individual profiles of users thatutilize device 130 to monitor the respective states of health of themonitored users. User data 134 can also include other data related to amonitored user, such as information related to one or more medicalprofessionals or services utilized by the user, emergency contactinformation (e.g., alternate phone numbers, e-mail addresses), insuranceinformation, etc. In one embodiment, a user data 134 includes aplurality of models associated with the determining/predicting a stateof health of the user, models associated with the consumption of foodand beverage, and other information that is substantially similar to thedata corresponding to the monitored user stored within a respectiveprofile of the user included in user data 104 of system 102. User datamay also include demographic data; information associated the physicalcondition of the user, such as height, weight, current medical issues;habits and activities of the user; dietary information; identified riskfactors, such as genetic testing data, etc. In some embodiments, userdata 134 does not include all the models associated with a user ormodels related to the consumption of food or beverages. Models may bedownloaded from system 102 as needed. In various embodiments, user data134 periodically receives information from device 140, such as anupdated medical history or results of visits to a medical professional.

Sensor data 135 includes raw and/or processed information from variousinstances of sensors 150. In one embodiment, sensor data 135 includesdata received from an instance of sensors 150 via communication path112. In some embodiments, sensor data 135 is periodically uploaded tosystem 102 for inclusion within a respective user profile in user data104. Upon storage within system 102, various portions of sensor data 135are deleted to prevent device 130 from becoming storage constrained. Inone embodiment, sensor data 135 buffers information from variousinstances of sensors 150 prior to processing by sensor data processingprogram 138.

Sensor data processing program 138 may include capabilities similar tosensor data processing program 108 of system 102. In some embodiments,based on the computational capabilities of device 130, sensor dataprocessing program 138 offloads the processing of sensor data to sensordata processing program 108 executing on system 102.

Sensors 150 are representative of one or more sensors that monitorvarious aspects of a user. Instances of sensors 150 may be batterypowered, inductively powered, or self-powered (e.g., by motion, by apiezoelectric effect, etc.). Some instances of sensors 150 can acquire avariety of sound-based monitoring information associated with a user ofdevice 130. In one example, sound-based monitoring information mayinclude audible sounds, sub-audible sounds, infra-sound, and ultrasoundwhen paired with an ultrasonic transducer. Other instance of sensors 150can include sensors that acquire other information, such as temperature,pressure, visual (e.g., pictures, video, visible colors, non-visiblecolors, etc.), movement, orientation, pulse, perspiration, neuralactivity (e.g., electrical activity) associated with one or moremuscles, and/or tension related to one or more muscles. Visualinformation acquired by an instance of sensors 150 may include images offood and beverages consumed by a user, images of the skin color and skincondition of the user, images of apparel worn by the user, and gesturesand expressions of the user. In one embodiment, user baseline program200 of device 130 receives information associated with one or moreinstances of sensors 150 is included in device 130.

In some embodiments, instances of sensors 150 are associated withacquiring information associated with the consumption of food orbeverage. In other embodiments, other instances of sensors 150 acquireinformation associated with factors within proximity to the user, suchas temperature, barometric pressure, humidity, etc. In variousembodiments, information obtained from one or more instances of sensors150 is processed by one or more aspects of a sensors data processingprogram, such as sensor data processing program 138 of device 130, toextract data that is input to one or more models.

In one embodiment, one or more instances of sensors 150 (e.g., a camera,a microphone) are embedded within device 130, such as a mobile phone ora personal fitness device. In another embodiment, one or more instancesof sensors 150 are included in another electronic or computing device(not shown) that communicates with or is linked to device 130, such as apair of smart glasses that utilize various capabilities of a smartphoneor tablet computer (e.g., device 130). In some embodiments, one or moreinstances of sensors 150 communicates information to device 130 viacommunication path 112. Some instances of sensors 150 directly monitorand obtain information associated with a user, an environment inproximity to the user, one or more activities of the user, and/or itemsassociated with a user, such as consumables (e.g., food, beverages,etc.). In other instances, sensors 150 may be embedded (e.g., hidden)within apparel, jewelry, accessories, etc. Still other instances,sensors 150 may be applied to the anatomy of a user and camouflaged witha covering matching the texture and tone of the skin of the user. In afurther embodiment, some instances of sensors 150 are not employed bythe user unless directed to do so by one or more aspects of the currentinvention and/or as directed by one or more medical professionals.

Device 140 includes user interface (UI) 142 and may also include variousprograms and data (not shown) utilized by device 140. Examples ofprograms associated with device 140 may include: an operating system, aweb browser; an office productivity suite; a database query program; anatural language processing program; one or more applications (apps),such as an instant messaging (IM) app, a telephone app, and a video chatapp; software to review and edit models; etc. Examples of dataassociated with device 140 may include, but are not limited to patient(i.e., user) records, emergency contacts, medical images, etc.

In an embodiment, device 140 is representative of a device associatedwith a medical professional. In some scenarios, device 140 is associatedwith a provider of medical services, such as an office of a doctor forthe user of device 130. In another scenario, device 140 is associatedwith an emergency response provider, such as a medical alert service oran ambulance service. In another embodiment, device 140 is a clientdevice by a group or a service provider that utilizes system 102 togenerate models that analyze information received from various instancesof sensors 150 during the monitoring of a plurality of users, and modelsfor predicting a state of health of a user based on the received sensorinformation. In addition, device 140 can enable the group or theadministrator of system 102 to access medical database(s) 125 of device120 to review and curate the corpus of medical information therein foraccess by system 102 and utilization within one or more models of users.

In one embodiment, UI 142 includes various functionalities andcapabilities described previously with respect to UI 132 of device 130.In another embodiment, UI 142 includes additional capabilities utilizedto access aspects of system 102, device 120, and/or one or more medicalservices. In an example, a doctor utilizes UI 142 to review anotification generated by an instance of medical monitoring program 300to determine whether to contact a user of device 130 to verify a stateof health of a user, to notify another individual associated with theuser (e.g., an emergency contact), and/or notify a medical service tocheck on the user.

FIG. 2 is a flowchart depicting operational steps for user baselineprogram 200, a program that generates and/or modifies models utilized topredict or determine a state of health for a monitored user, inaccordance with embodiments of the present invention. In variousembodiments, user baseline program 200 obtains information frominstances of sensors 150 and information input by the user associatedwith one or more actions, observations, and/or feedback associated withthe user and the sensors monitoring the user to aggregate within acorpus of information used for generating models associated with theuser. In an embodiment, user baseline program 200 executes at varioustimes or as dictated by the user.

In some embodiments, instances of user baseline program 200 executeconcurrently with one or more instances of medical monitoring program300. In other embodiments, user baseline program 200 executes inresponse to certain conditions, such as medical monitoring program 300initiating the execution of an instance of user baseline program 200 toobtain user feedback and modify a model. In a further embodiment, aninstance of user baseline program 200 executes on system 102 on aperiodic basis or as dictated by administrators of system 102 and/ormedical professionals that utilize aspects of the present invention tomonitor patients.

In step 202, user baseline program 200 receives information associatedwith a sensor. User baseline program 200 receives information from aplurality of instances of sensors 150 associated with a monitored user.Sensor-based user information received by user baseline program 200 mayinclude images of the food and beverages consumed by a user, images ofthe appearance (e.g., skin color and condition, apparel worn by theuser, gestures and expressions of the user). In one embodiment, userbaseline program 200 of device 130 receives information associated withone or more instances of sensors 150 is included in device 130. Inanother embodiment, user baseline program 200 of device 130 receivesinformation associated with one or more instances of sensors 150 viacommunication path 112. In another embodiment, user baseline program 200of system 102 receives information associated with one or more instancesof sensors 150 via communication path 114 and network 110.

In some embodiments, user baseline program 200 obtains informationassociated with one or more sensors received during the execution of aninstance of medical monitoring program 300 (referring to FIG. 3, step302). In some scenarios, user baseline program 200 obtains informationassociated with one or more sensors included in sensor data 135 ofdevice 130. In other scenarios, user baseline program 200 obtainsinformation associated with one or more sensors stored in user data 104of system 102. In various scenarios, user baseline program 200 obtainsinformation associated with one or more sensors from a combination ofstorage locations.

In step 204, user baseline program 200 receives information from a user.In one embodiment, during a training period, a user utilizes device 130to input information related to a consumption of food and/or beverage.User baseline program 200 may utilize UI 132 to present a questionnaireto the monitored user of device 130 to obtain information. Thequestionnaire includes queries associated with the current state ofhealth of a user, a location (e.g., sitting outdoors, in a restaurant,riding in a vehicle, etc.), a current or historic level of stress, foodor beverage to consume, the attire of the user, etc.

In another embodiment, user baseline program 200 receives informationfrom a user based on processing one or more queries associated withvarious aspects of medical monitoring program 300. In one scenario, userbaseline program 200 receives information from a user related to medicalmonitoring program 300 determining that the information (e.g., sensorinformation and/or user input information) does not fit a model(referring to FIG. 3, No branch of decision step 307). In an example,medical monitoring program 300 expects a range of sensor information(i.e., values) for a model based on one set of information, such as afood choice, input by the user. However, the actual food consumed by theuser is not associated with the input information. In another scenario,user baseline program 200 receives information from a user in responseto medical monitoring program 300 determining that a model associatedwith a user does not indicates a normal state for the user (referring toFIG. 3, No branch of decision step 309), such as the user flagging afalse alarm.

Still referring to step 204 in some embodiments, user baseline program200 receives information from another individual based on medicalmonitoring program 300 determining or predicting one state and/or levelof urgency associated with a user (referring to FIG. 3, No branch ofdecision step 309). However, the actual state of the user differs fromthe state of the user determined and/or predicted by one or more models.In one example, a doctor reviews a notification generated by medicalmonitoring program 300 and the received sensor data, and the doctordetermines that a model is generating a false-positive result. If thedoctor contacts the user and verifies that the model generated afalse-positive result, then the doctor may flag the model for modifying.Alternatively, based on feedback from the user, medical monitoringprogram 300 may determine that a model underestimates a level of urgencyassociated with a predicted change in the state of health of the user.

In step 206, user baseline program 200 analyzes the receivedinformation. In one embodiment, user baseline program 200 utilizes aninstance of sensor data processing program 138 of device 130 or sensordata processing program 108 of system 102 to analyze and/or preprocessthe information associated with one or more instances of sensors 150. Inanother embodiment, user baseline program 200 utilizes one or moreaspects of analytics suite 106 to process the received information, suchas an NLP program to parse and analyze information input by the user,such as a response to a questionnaire presented by UI 132. The responsesto the questionnaire are subsequently utilized during the generation ormodification of one or more models. In various embodiments, userbaseline program 200 utilizes one or more aspects of analytics suite 106to perform image recognition to determine information related to one ormore received images, such as identifying a quantity and type of foodconsumed by the user, or skin-related information (e.g., flushing,pigmentation change, etc.). In other embodiments, user baseline program200 analyzes information from other sources, such as weather conditions,pollen counts, a time & date function, etc.

In step 210, user baseline program 200 obtains historical data.Historical data refers to monitoring data and medical-relatedinformation associated with a user. Historical data may consist of thefollowing types of information: sounds, models, sensor information,associated times & dates, locations of the user, user information, itemsof food or beverages consumed, information obtained from medicaldatabase(s) 125 of device 120, medical history of the user, diagnostictesting data related to the user, etc. In one embodiment, if device 130is storage constrained, then user baseline program 200 obtains one ormore models from models 105 of system 102 based on a corpus ofinformation associated with the user, user input, and/or preliminarysensor information that indicates a consumption of food or beverage. Inanother embodiment, user baseline program 200 utilizes a clock orcalendar function to determine a set of models to be available on device130. In some embodiments, in response to medical monitoring program 300determining that a model is modified, user baseline program 200 obtainshistorical information from user data 104 of system 102, such as sounds,models, sensor information, etc. for utilization by machine learningprogram 107 to modify one or more models (step 213).

In decision step 211, user baseline program 200 determines whether tomodify a model associated with a user. In one embodiment, user baselineprogram 200 determines to make a modification to a model associated witha user based on one or more actions of an instance of medical monitoringprogram 300 (e.g., determining that a model does not describe a state ofthe user). In another embodiment, user baseline program 200 determinesto make a modification to a model associated with a user based onreceiving updated information from one or more sources, such asnew/updated information within medical database(s) 125 of device 120, ormodels of similar users are updated within system 102. In someembodiments, user baseline program 200 receives a dictate (e.g., acommand, a response) from a medical professional via UI 142 of device140 to modify (e.g., update) a model. In other embodiments, userbaseline program 200 generates one or more models related to the user asopposed to modifying a model. For example, user baseline program 200generates a model during a training period associated with the user orin response to determining that a user consumes a food or beverage thatis not modeled. In an alternative embodiment, if user baseline program200 does not have sufficient data and information to modify a model orgenerate a model, then user baseline program 200 terminates.

Responsive to determining not to modify a model associated with a user amodel associated with the user (No branch, decision step 211), userbaseline program 200 generates one or more models related to the user(step 212).

In step 212, user baseline program 200 generates one or more modelsrelated to the user. User baseline program 200 may utilize a combinationof functions and/or programs associated with machine learning program107 and analytics suite 106 to generate one or more models related tothe user. In one embodiment, user baseline program 200 generates modelsrepresenting the consistency of food and/or beverage consumed by theuser under known conditions and a user input state of health. In anotherembodiment, user baseline program 200 generates a model representativeof a state of health of the user by utilizing one or more food models,information within medical database(s) 125 of device 140, andinformation input by the user. In some embodiments, user baselineprogram 200 generates a model related to a user by tailoring a model,stored within models 105, associated with one or more similar users. Ina further embodiment, user baseline program 200 transmits a query andone or more models to a medical professional via device 140 for review,modifying, and approval.

Referring to decision step 211, responsive to determining to modify oneor more models (Yes branch, decision step 211), user baseline program200 modifies one or more models (step 213).

In step 213, user baseline program 200 modifies one or more models. Userbaseline program 200 may utilize a combination of functions and/orprograms associated with machine learning program 107 and analyticssuite 106 to modify, update, or replace one or more models. In variousembodiments, user baseline program 200 utilizes information obtained bymedical monitoring program 300 to modify a model. In one embodiment,user baseline program 200 modifies one or more models representing theconsistency of food and/or beverage consumed by the user underconditions and states of health not previously observed or cataloged. Inanother embodiment, user baseline program 200 modifies a model that isnot necessarily representative of the state of health of the user. Insome embodiments, user baseline program 200 modifies a model based onnew or updated information within medical database(s) 125 of device 120.In a further embodiment, user baseline program 200 transmits a query andone or more models to a medical professional via device 140 for review,modifying, and approval.

In step 214, user baseline program 200 stores information associatedwith the user. In one embodiment, user baseline program 200 stores thedata received from one or more sensors and the information received froma user within one or more storage locations, such as user data 104 ofsystem 102, user data 134, and/or sensor data 135 of device 130. Inanother embodiment, user baseline program 200 stores one or moregenerated models related to the user within one or more storagelocations, such as user data 104 of system 102, user data 134, and/orsensor data 135 of device 130. In some embodiments, user baselineprogram 200 periodically uploads information from portions of sensordata 135 of device 130 prior to deleting sensor information to reclaimstorage space on system 130. In other embodiments, another individual,such as a medical professional, can override which information is storedby user baseline program 200.

FIG. 3 is a flowchart depicting operational steps for medical monitoringprogram 300, a program for determining a state of health associated withthe jaw, neck, and throat regions of a monitored user, in accordancewith embodiments of the present invention. In various embodiments,medical monitoring program 300 determines the current state of health ofa user or predicts a change to the state of health of the user based onsensors recording various sounds during deglutition by the user. In someembodiments, medical monitoring program 300 utilizes other types ofsensor information, such as sound as input(s) to one or more models todetermine the current state of health of a user or predict a change tothe state of health of the user. An instance of medical monitoringprogram 300 can execute concurrently with one or more instances of userbaseline program 200.

In step 302, medical monitoring program 300 receives informationassociated with a sensor. Medical monitoring program 300 receivesinformation from one or more instances of sensors 150 associated with auser of device 130 as previously discussed with respect to FIG. 2, step202. In some embodiments, medical monitoring program 300 receivesatypical sensor information that was not identified during the trainingand generation of one or more models, such as an indication of choking;or the user utilizing a back slap, chest thump, or an intake of extraliquid (i.e., a beverage) to facilitate swallowing.

In step 304, medical monitoring program 300 determines informationassociated with a user. In an embodiment, medical monitoring program 300determines information associated with a user of device 130 aspreviously discussed with respect to FIG. 2, step 204. In someembodiments, medical monitoring program 300 determines information(e.g., analyzes data) associated with a user of device 130 as previouslydiscussed with respect to FIG. 2, step 206. In various embodiments,based on the determined information, medical monitoring program 300identifies the one or more models to utilize with respect to the currentconditions associated with the user. In an example, based on an imagerecognition program (not shown) determining the food for consumption bythe user and information input by the user, medical monitoring program300 identifies one or more models to utilize for determining the stateof health of the user or predicting a change to the state of health ofthe user. The identified models may be included within user data 134 ofdevice 130 and/or models 105 of system 102.

In step 306, medical monitoring program 300 utilizes a model associatedwith a user. In one embodiment, medical monitoring program 300 utilizesone or more identified models to obtain results that describe (e.g.,predict) the anticipated information from various instances of sensors150 based on the food and/or beverage to be consumed by the user andother factors associated with the user, such as time & date, location,environmental factors, and apparel of the user. In another embodiment,an instance of medical monitoring program 300 executing on device 130can download models as needed from system 102 that are not stored withinuser data 134. In some embodiments, medical monitoring program 300inputs the results derived from models associated with consumption offood and beverages to one or more models associated with determining astate of health of the user and/or predicting a change to the state ofhealth of the user. In other embodiments, medical monitoring program 300utilizes one or more identified models associated with the user todetermine whether the information received from one or more instances ofsensors 150 matches the expected range of sensor information (i.e.,values).

In decision step 307, medical monitoring program 300 determines whetherthe information fits a model. In one embodiment, medical monitoringprogram 300 determines, based on the information received and analyzedfrom one or more instances of sensors 150, that the information does notfit a model. In one example, medical monitoring program 300 utilizes amodel based on the input of the user. However, one or more attributesassociated with food and/or beverage (e.g., an expected range of sensorinformation/values), such as consistency, bite force, chewing duration,etc., does not match output of actions that the user performs to consumethe food and/or beverages input by the user. In another example, medicalmonitoring program 300 determines that information associated with theuser is not included in the model, such as the user eating while wearingconstricting neckwear, or the user eating in an orientation that was notmodeled (e.g., laying horizontal). In another embodiment, medicalmonitoring program 300 determines that a model does not exist for theinformation input, such as a new type of food. In some embodiments,medical monitoring program 300 includes one or more override conditionsthat bypass (i.e., skip) decision step 307 and trigger the No branch ofdecision step 309, such as an episode of choking that exceeds athreshold value, such as 5 seconds; or a change to the color of the skinof a user that may indicate the occurrence of cyanosis.

In response to determining that the information does not fit a model (Nobranch, decision step 307), medical monitoring program 300 modifies oneor more models (step 308).

In step 308, medical monitoring program 300 modifies a model. In oneembodiment, medical monitoring program 300 executes an instance of userbaseline program 200 to modify one or more models and/or generate one ormore new models associated with the food and/or beverage consumed by theuser. In another embodiment, medical monitoring program 300 executes aninstance of user baseline program 200 to modify one or more modelsassociated with the state of the monitored user and additionalinformation input by the user.

Referring to decision step 307, in response to determining that theinformation fits a model (Yes branch, decision step 307), medicalmonitoring program 300 determines whether a model indicates a normalstate of health for the user (decision step 309).

In decision step 309, medical monitoring program 300 determines whethera model indicates a normal state for the user. Medical monitoringprogram 300 can review and analyze the results of multiple models inparallel. Different models are utilized to determine or predict variouschanges to a state of health of the user. In various embodiments,medical monitoring program 300 determines that a single abnormal result(e.g., a state of health or a predicted change to the state of health ofthe user) is obtained and triggers the No branch of decision step 309.In one embodiment, medical monitoring program 300 determines that amodel describes a normal state of health for a user. In anotherembodiment, medical monitoring program 300 determines whether a model,modified or newly generated by user baseline program 200 (step 308),describes a normal state of health for the user.

In response to determining that a model does not indicate a normal statefor the user (No branch, decision step 309), medical monitoring program300 optionally queries the user for additional information (step 310).In some embodiments, based on which one or more models do not indicate anormal state of health for the user, medical monitoring program 300modifies the execution order of steps 310 through 316.

In step 310, medical monitoring program 300 optionally queries the userfor additional information. In one embodiment, medical monitoringprogram 300 queries the monitored user via device 130 to determinewhether information associated with one or more sensors is correct. Inone example, the user may be wearing a pair of glasses with one or moreembedded sensors on top of the head of the user as opposed to the faceof the user. Medical monitoring program 300 may determine that theresults associated with the sensors of the glasses are suspect andcontact the user via device 130 to determine whether the glasses of themonitored user are worn correctly prior to making furtherdeterminations. In another example, medical monitoring program 300queries the user to determine a reason for biased sensor information,such as chewing preferentially on one side of the mouth of the user. Insome embodiments, medical monitoring program 300 queries a user lessfrequently based on one or more modified models. In an example, machinelearning program 107 identifies user actions and/or behaviors thatpotentially trigger a query and includes the identified user actionsand/or behaviors to one or more models of models 105 and/or modelswithin user data 134.

In another embodiment, medical monitoring program 300 queries a user toobtain information that may indicate a reason that a model predicts achange to the state of health of the user. In an example, medicalmonitoring program 300 queries a user to determine whether the user isafflicted with a minor medical condition, such as an episode ofallergies, an occurrence of a cold or flu, stomach problems, etc.

In step 312, medical monitoring program 300 determines a level ofurgency associated with the state of health of the user. In addition todetermining a level of urgency, medical monitoring program 300 cantransmit one or more notifications related to the current or predictedfuture state of health of the user. Based on the level of urgency,medical monitoring program 300 can send a notification to the user, oneor more medical professionals, and/or an emergency medical service. Inone embodiment, medical monitoring program 300 determines a level ofurgency associated with the state of health of the user based on a knownmedical condition of the user and one or more results generated bymodels associated with the user. In one example, medical monitoringprogram 300 may notify a user, via device 130, to contact the primarycare provider (PCP) for the user and provide the user with an indicationof the level of urgency while excluding one or more possible diagnoses.However, medical monitoring program 300 also notifies the PCP via UI 142of device 140 with a determined level of urgency and various possiblediagnoses related to the state of health of the user. Medical monitoringprogram 300 thereby enables the PCP to order diagnostic tests for theuser prior to the user visiting the office of the PCP.

In another embodiment, medical monitoring program 300 queries medicaldatabase(s) 125 of device 120 to determine a level of urgency associatedwith the determined state of health of the user. In an example, medicalmonitoring program 300 utilizes the results of one or more models andassociated sensor information to query medical database(s) 125 of device120 to determine a level of urgency associated with the current state ofheath of the user. In some embodiments, medical monitoring program 300queries medical database(s) 125 of device 120 to determine a level ofurgency associated with a predicted change to the state of health of theuser. In an example, medical monitoring program 300 predicts thepossibility of a change to the state of the health of the user. However,medical monitoring program 300 does not have sufficient information topropose one or more possible diagnosis with a high level of confidence.Therefore, medical monitoring program 300 transmits a notification to amedical professional associated with the user to obtain furtherinformation related to the state of the user (step 314), such asordering diagnostic testing.

In step 314, medical monitoring program 300 obtains information relatedto the state of the user. In some embodiments, medical monitoringprogram 300 queries a user via UI 132 to obtain information related tothe state of the user. Medical monitoring program 300 communicates anotification of a determined or predicted state of health (e.g., anassessment) associated with the user and a corresponding level ofurgency related to the health of the user. Medical monitoring program300 may obtain information similar to the information discussed withrespect to step 310. In addition, medical monitoring program 300 mayquery the user for more specific information related to aspects of thelevel of urgency as perceived by the user, such as an opinion related tothe accuracy of the level of urgency or other information may indicateinaccuracies within one or more models. In other embodiments, if medicalmonitoring program 300 cannot obtain information related to the userfrom the user, then medical monitoring program 300 communicates withanother individual, such as an emergency contact, or a known individualin proximity to the user (e.g., based on location information and asocial networking application). In various embodiments, medicalmonitoring program 300 obtains additional information related to thestate of the user from one or more medical services, such as a doctor ofa user, a medical testing location, emergency personnel interacting withthe user, etc.

In step 316, medical monitoring program 300 optionally modifies a model.In one embodiment, if medical monitoring program 300 determines that amodel produced a false-positive result, such a predicting a negativechange to the health of the user or over estimating the level ofurgency; then, medical monitoring program 300 executes an instance ofuser baseline program 200 to modify a model. In another embodiment, ifmedical monitoring program 300 determines that a model underestimatedthe level of urgency for a state of health of the user, then medicalmonitoring program 300 executes an instance of user baseline program 200to modify a model. In some embodiments, medical monitoring program 300modifies one or more models associated with the consumption of foodand/or beverage. In other embodiments, medical monitoring program 300modifies a model based on a dictate (e.g., input, command) by a medicalprofessional. Subsequently, medical monitoring program 300 determineswhether to terminate the monitoring of the user (decision step 317).

Referring to decision step 309, responsive to determining that a modelsindicates a normal state of health of the user (Yes branch, decisionstep 309), medical monitoring program 300 determines whether toterminate the monitoring of the user (decision step 317).

In decision step 317, medical monitoring program 300 determines whetherto terminate the monitoring of the user. In some embodiments, medicalmonitoring program 300 continually monitors a user. In otherembodiments, to conserve battery life of device 130, medical monitoringprogram 300 terminates monitoring the user based on one or moretriggers, such as a time of day, lack of activity by the user, betweensampling periods while the user is not consuming food or beverage, etc.In one example, medical monitoring program 300 terminates monitoring theuser based on a period after the user stops consuming food or beverage,a clock function deactivating at a preprogrammed time slots, or asdictated by the user (e.g., via UI 132). In another example, medicalmonitoring program 300 terminates monitoring the user based on the userremoving one or more instances of sensors 150 from the person of theuser. In one embodiment, medical monitoring program 300 does notterminate monitoring the user in response to determining that device 130is connected to a persistent power source, such as a charger.

Responsive to determining not to terminate monitoring the user (Nobranch, decision step 317), medical monitoring program 300 loops to step302.

Responsive to determining to terminate monitoring the user (Yes branch,decision step 317), medical monitoring program 300 stops executing.

FIG. 4 depicts a block diagram of computer system 400, which isrepresentative of system 102, device 120, device 130, and device 140.Computer system 400 is an example of a system that includes software anddata 412. Computer system 400 includes processor(s) 401, memory 402,cache 403, persistent storage 405, communications unit 407, input/output(I/O) interface(s) 406, and communications fabric 404. Communicationsfabric 404 provides communications between memory 402, cache 403,persistent storage 405, communications unit 407, and I/O interface(s)406. Communications fabric 404 can be implemented with any architecturedesigned for passing data and/or control information between processors(such as microprocessors, communications and network processors, etc.),system memory, peripheral devices, and any other hardware componentswithin a system. For example, communications fabric 404 can beimplemented with one or more buses or a crossbar switch. In someembodiments, computer system 400 is also representative of someinstances of sensors 150.

Memory 402 and persistent storage 405 are computer readable storagemedia. In this embodiment, memory 402 includes random access memory(RAM). In general, memory 402 can include any suitable volatile ornon-volatile computer readable storage media. Cache 403 is a fast memorythat enhances the performance of processor(s) 401 by holding recentlyaccessed data, and data near recently accessed data, from memory 402.

Program instructions and data used to practice embodiments of thepresent invention may be stored in persistent storage 405 and in memory402 for execution by one or more of the respective processor(s) 401 viacache 403. In an embodiment, persistent storage 405 includes a magnetichard disk drive. Alternatively, or in addition to a magnetic hard diskdrive, persistent storage 405 can include a solid-state hard drive, asemiconductor storage device, a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM), a flash memory, or any othercomputer readable storage media that is capable of storing programinstructions or digital information. In an embodiment, with respect tosystem 102, storage 103 is included in persistent storage 405 and withrespect to device 130 storage 133 is included in persistent storage 405.

The media used by persistent storage 405 may also be removable. Forexample, a removable hard drive may be used for persistent storage 405.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of persistent storage405. Software and data 412 are stored in persistent storage 405 foraccess and/or execution by one or more of the respective processor(s)401 via cache 403 and one or more memories of memory 402. With respectto system 102, software and data 412 includes: user data 104, models105, analytics suite 106, machine learning program 107, sensor dataprocessing program 108, user baseline program 200, medical monitoringprogram 300, and various programs (not shown). With respect to device120, software and data 412 includes medical database(s) 125 and variousprograms and data (not shown). With respect to device 130, software anddata 412 includes user data 134, sensor data 135, sensor data processingprogram 138, user baseline program 200, medical monitoring program 300,and various programs and data (not shown). With respect to device 140,software and data 412 includes UI 142 and various programs and data (notshown). With respect to some instances of sensors 150, software and data412 may be representative of firmware (not shown) utilized to operate aninstance of sensors 150.

Communications unit 407, in these examples, provides for communicationswith other data processing systems or devices, including resources ofsystem 102, device 120, device 130, device 140, and instances of sensors150. In these examples, communications unit 407 includes one or morenetwork interface cards and/or one or more wireless communication units.Communications unit 407 may provide communications through the use ofeither or both physical and wireless communications links. Programinstructions and data used to practice embodiments of the presentinvention may be downloaded to persistent storage 405 throughcommunications unit 407.

I/O interface(s) 406 allows for input and output of data with otherdevices that may be connected to each computer system. For example, I/Ointerface(s) 406 may provide a connection to external device(s) 408,such as a keyboard, a keypad, a touch screen, one or more instance ofsensors 150, and/or some other suitable input device. External device(s)408 can also include portable computer readable storage media, such as,for example, thumb drives, portable optical or magnetic disks, andmemory cards. Software and data 412 used to practice embodiments of thepresent invention can be stored on such portable computer readablestorage media and can be loaded onto persistent storage 405 via I/Ointerface(s) 406. I/O interface(s) 406 also connect to display 409.

Display 409 provides a mechanism to display data to a user and may be,for example, a computer monitor. Display 409 can also function as atouch screen, such as the display of a tablet computer or a smartphone.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Additionally, the phrase “based on” should be interpreted to mean“based, at least in part, on.”

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A method for identifying a change associated witha state of health of a user, the method comprising: receiving, by one ormore computer processors, monitoring data associated with monitoring auser, wherein the monitoring data is generated by one or more sensors;determining, by one or more computer processors, a state of health ofthe monitored user by analyzing the monitoring data utilizing one ormore models; determining, by one or more computer processors, a level ofurgency based, at least in part, upon the determine state of health ofthe monitored user; and transmitting, by one or more computerprocessors, one or more respective notifications to one or more devicesbased, at least in part, on the determined state of health of the userand the corresponding level of level of urgency, wherein the one or moredevices includes a device associated with the monitored user, andwherein a notification include a determined state of health and thecorresponding determined state of urgency associated with the monitoreduser.
 2. The method of claim 1, wherein receiving data associated withmonitoring the user further comprises: preprocessing, by one or morecomputer processors, the data received from a sensor utilizing one ormore programs, wherein the one or more programs include a conversionprogram and an analytical program; and inputting, by one or morecomputer processors, the preprocessed data from the sensor to one ormore models associated with determining the state of health of themonitored user.
 3. The method of claim 1, wherein determining a state ofhealth of the monitored user by analyzing the monitoring data utilizingone or more models, further comprises: determining, by one or moreprocessors, a predicted change associated with the state of health ofthe monitored user by analyzing the monitoring data utilizing one ormore models.
 4. The method of claim 1, further comprising: determining,by one or more computer processors, that a model does not determine astate of health of the monitored user; in response to determining that amodel does not determine the state of health of the monitored user,communicating, by one or more computer processors, with the deviceassociated with the monitored user to obtain information from themonitored user; receiving, by one or more computer processors,information input to the device of the monitored user, wherein thereceived information includes feedback related to a state of health, asinterpreted by the monitored user and information related to thereceived monitoring data; and modifying, by one or more computerprocessors, one or more models of the monitored user based, at least inpart, on the received information and one or more machine learningalgorithms.
 5. The method of claim 1, wherein receiving monitoring dataassociated with monitoring the user further comprises: determining, byone or more computer processors, information associated with theconsistency of food consumed by the monitored user based on receivinginformation input by the user and information received from one or moresensors; and analyzing, by one or more processors, data related to oneor more actions associated with the monitored user consuming the food,wherein data related to one or more actions associated with themonitored user consuming the food includes a duration of time associatedwith consuming a portion of food and one or more sounds producedconsuming the portion of food.
 6. The method of claim 3, furthercomprising: in response to determining that a model does not determinethe state of health of the monitored user, identifying, by one or morecomputer processors, one or more behaviors of the monitored user relatedto the consumption of food; and wherein one or more behaviors themonitored user related to the consumption of food are selected from thegroup consisting of preferentially chewing in a portion of the mouth, asize of the portion of food within the mouth of the monitored user, asequence of actions associated with consuming food and beverage.
 7. Themethod of claim 1, wherein the monitoring data includes informationrelated to one or more physical characteristics of neck and throatregion of the monitored user, selected from the group consisting of:information associated with a skin condition, a degree of tension in oneor more muscles, one or more lumps under the skin, and a physicalorientation of the monitored user; and wherein the monitoring dataincludes environmental factors associated with the monitored user,selected from the group consisting of: one or more items of apparel inproximity to the neck and throat region, a temperature in proximity tothe monitored user, and a level of stress associated with the monitoreduser.